In what follows, I appeal to Charles Babbage’s discussion of the division of mental labor to provide evidence that—at least with respect to the social acquisition, storage, retrieval, and transmission of knowledge—epistemologists have, for a broad range of phenomena of crucial importance to actual knowers in their epistemic practices in everyday life, failed adequately to appreciate the significance of socially distributed cognition. If the discussion here is successful, I will have demonstrated that a particular presumption widely held within the contemporary (...) discussion of the epistemology of testimony—a presumption that I will term the personalist requirement—fails to account for those very practices of knowers that I detail here. I will then conclude by suggesting that an alternate account of testimonial warrant, one that has heretofore been underappreciated, ought to be given more serious consideration—in particular because it is well suited to account for those actual practices of knowers that the personalist requirement leaves unrecognized. (shrink)

Through an analysis of university students’ job-hunting logs, we have found that their introspection via rereading their log sometimes helps them discover themselves. Then we have built a system called PLASIU designed to support job-hunter’s creative decision-making based on the observations from their actual job-hunting process. This paper provides an overview of PLASIU and describes the findings from a user study using PLASIU.

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real‐time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1–6. Then we show (...) differences in overconfidence about knowledge across different knowledge domains in Studies 7–10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12, and discuss the implications of our findings for the roles of intuitive theories in concepts and cognition. (shrink)

This study compares Pairs of subjects with Single subjects in a task of discovering scientific laws with the aid of experiments. Subjects solved a molecular genetics task in a computer micro‐world (Dunbar, 1993). Pairs were more successful in discovery than Singles and participated more actively in explanatory activities (i.e., entertaining hypotheses and considering alternative ideas and justifications). Explanatory activities were effective for discovery only when the subjects also conducted crucial experiments. Explanatory activities were facilitated when paired subjects made requests of (...) each other for explanation and focused on them. The study extends from individual to collaborative discovery activities the importance to the discovery process of setting goals to find hypotheses and evidence (Dunbar, 1993) and to construct explanations of phenomena and processes encountered in examples (Chi, Bassok, Lewis, & Glaser, 1989). (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Integrating different perspectives is a sophisticated strategy for developing constructive interactions in collaborative problem solving. However, cognitive aspects such as individuals’ knowledge and bias often obscure group consensus and produce conflict. This study investigated collaborative problem solving, focusing on a group member interacting with another member having a different perspective. It was predicted that mavericks might mitigate disadvantages and facilitate perspective taking during problem solving. Thus, 344 university students participated in two laboratory-based experiments by engaging in a simple rule-discovery task (...) that raised conflicts among perspectives. They interacted with virtual partners whose conversations were controlled by multiple conversational agents. Results show that when participants interacted with a maverick during the task, they were able to take others’ perspectives and integrate different perspectives to solve the problem. Moreover, when participants interacted in groups with a positive mood, groups with a maverick outperformed groups having several perspectives. (shrink)

This paper offers an explanation of how collaboration leads to abstract and flexible problem solving. We asked the individual and paired subjects to indicate 3/4 of 2/3 of the area of a square sheet of paper and found that (1) they primarily folded or partitioned the paper rather than algorithmically calculating the answer, (2) they strongly tendened to backtrack and confirm their proto‐plans on externalized traces such as creases on the paper, and (3) only the paired subjects shifted to the (...) mathematical strategy in their second trials. Based on these results, we propose that two factors, individuals' activeness in choosing and confirming the initial strategies and the frequent role exchange between task‐doing and monitoring in collaborative situations, interact in collaboration to generate various solutions differing in the degree of abstraction, which are then reflected upon by the participants to lead them to abstraction. (shrink)

This work analyzes the central role of interpretation in non-routine design. Based on this analysis, a theory of computer support for interpretation in cooperative design is constructed. The theory is grounded in studies of design and interpretation. It is illustrated by mechanisms provided by a software substrate for computer-based design environments, applied to a sample task of lunar habitat design. ;Computer support of innovative design must overcome the problem that designers necessarily make extensive use of situated tacit understanding while computers (...) can only store and display explicit representations of information. The automation techniques used for routine design are not applicable: techniques are needed to support creative, tacit human understanding with explicit computer representations. ;The process by which designers transform their tacit preunderstanding into explicit knowledge is termed "interpretation". Interpretation is necessary for solving design problems and collaborating with other designers. Considerable explicit knowledge is thereby generated in the natural course of designing. Often this knowledge includes the most valuable information that can be presented to designers who revisit these design projects or undertake similar projects in the future. If representations of this knowledge have been defined using computer-based design support systems, then the representations can be captured by these systems for the support of subsequent design work. ;A theory of computer support for interpretation in design is presented in three stages. First, the role of interpretation in design is explored by reviewing descriptions of design by Alexander, Rittel, and Schon; by conducting a protocol analysis of lunar habitat design; and by applying Heidegger's philosophy of situated interpretation. Second, this analysis of interpretation is extended to define a theory of computer support. The features of this theory--support for the situated, perspectival, and linguistic characteristics of interpretation--are used to evaluate previous work on software design rationale systems. Third, design principles are discussed for H scERMES, a prototype hypermedia substrate for building computer-based design environments to support interpretation in tasks like lunar habitat design. The hypermedia integrates a perspectives mechanism and an end-user language to capture and modify representations of the design situation, alternative perspectives on design tasks, and terminology for conceptualizing design issues. (shrink)

In the course of our educational life we are introduced to various subject areas, each with its specific way of representing knowledge. The challenge for the learner is to be able to think in ways that are supported by, and match, the representational format. A fundamental question for the science of learning concerns how this is achieved. In this thesis, it will be argued that by observing individuals collaboratively constructing their own graphic representations in a subject area that is new (...) to them, it is possible to gain insight into this process. In the LUMES Master program, students are introduced to systems thinking and the method of creating Causal Loop Diagrams and interactive computer models for analyzing and solving complex problems. They learn how to use the terminology, symbols and methods of a specific semiotic domain. The research questions guiding the analysis of the empirical data concern how interacting with systems thinking tools affect students in the process of collaboratively analyzing and modeling complex problems, and how cognitive and semiotic resources are used in this process. The study provides a conceptual lens with which to discuss and perceive the complexity of learning to express knowledge with new representational tools: both its visual and spatial formalism and the related terminology. Three theoretical perspectives are applied: the socio-cultural perspective, the social semiotic, and the constructivist perspective. The sociocultural perspective is applied to emphasize the use of tools as a part of human learning. It puts focus on the mediating roles of language and other tools in a social context, which is in many ways connected to the social semiotic perspective. To view systems thinking and System Dynamics as a semiotic domain accentuates the use of specific terminology, symbols and gestures to express understanding. Furthermore, the constructivist perspective is used to put focus on the role of individual cognitive abilities and prior knowledge. Three empirical studies provide detailed analyses of video-recorded collaborative work in System Dynamics education. The case studies show how individual cognitive abilities together with language, gesture and the visual representations are used as integrated resources to express understanding of a problem and its solution. (shrink)